SOTAVerified

Image Captioning

Image Captioning is the task of describing the content of an image in words. This task lies at the intersection of computer vision and natural language processing. Most image captioning systems use an encoder-decoder framework, where an input image is encoded into an intermediate representation of the information in the image, and then decoded into a descriptive text sequence. The most popular benchmarks are nocaps and COCO, and models are typically evaluated according to a BLEU or CIDER metric.

( Image credit: Reflective Decoding Network for Image Captioning, ICCV'19)

Papers

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Showing 1751–1800 of 1878 papers

TitleStatusHype
Learning to Relate from Captions and Bounding Boxes—0
Learning to Select: A Fully Attentive Approach for Novel Object Captioning—0
Learning Visual-Linguistic Adequacy, Fidelity, and Fluency for Novel Object Captioning—0
Learning Visual Representations with Caption Annotations—0
Learning Word Embeddings for Low-Resource Languages by PU Learning—0
Let's Go Shopping (LGS) -- Web-Scale Image-Text Dataset for Visual Concept Understanding—0
"Let's not Quote out of Context": Unified Vision-Language Pretraining for Context Assisted Image Captioning—0
Leveraging Partial Dependency Trees to Control Image Captions—0
Leveraging Sentence Similarity in Natural Language Generation: Improving Beam Search using Range Voting—0
Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-Modal Knowledge Transfer—0
Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-modal Knowledge Transfer—0
Lexical Simplification with the Deep Structured Similarity Model—0
LG-VQ: Language-Guided Codebook Learning—0
Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models—0
Lightweight In-Context Tuning for Multimodal Unified Models—0
Linguistically-aware Attention for Reducing the Semantic-Gap in Vision-Language Tasks—0
利用图像描述与知识图谱增强表示的视觉问答(Exploiting Image Captions and External Knowledge as Representation Enhancement for Visual Question Answering)—0
LLaMA-Excitor: General Instruction Tuning via Indirect Feature Interaction—0
LLARVA: Vision-Action Instruction Tuning Enhances Robot Learning—0
LLM4VG: Large Language Models Evaluation for Video Grounding—0
LLMs Can Check Their Own Results to Mitigate Hallucinations in Traffic Understanding Tasks—0
LocCa: Visual Pretraining with Location-aware Captioners—0
Longer Version for "Deep Context-Encoding Network for Retinal Image Captioning"—0
Long-Tail Classification for Distinctive Image Captioning: A Simple yet Effective Remedy for Side Effects of Reinforcement Learning—0
Look Back and Predict Forward in Image Captioning—0
Look Deeper See Richer: Depth-aware Image Paragraph Captioning—0
LookupViT: Compressing visual information to a limited number of tokens—0
Lost in Translation: When GPT-4V(ision) Can't See Eye to Eye with Text. A Vision-Language-Consistency Analysis of VLLMs and Beyond—0
LVLM_CSP: Accelerating Large Vision Language Models via Clustering, Scattering, and Pruning for Reasoning Segmentation—0
Lyrics: Boosting Fine-grained Language-Vision Alignment and Comprehension via Semantic-aware Visual Objects—0
M3D-GAN: Multi-Modal Multi-Domain Translation with Universal Attention—0
Macroscopic Control of Text Generation for Image Captioning—0
MAGIC: Multimodal relAtional Graph adversarIal inferenCe for Diverse and Unpaired Text-based Image Captioning—0
MAGNet: Multi-Region Attention-Assisted Grounding of Natural Language Queries at Phrase Level—0
Making the Most of What You Have: Adapting Pre-trained Visual Language Models in the Low-data Regime—0
Making Use of Latent Space in Language GANs for Generating Diverse Text without Pre-training—0
MAMI: Multi-Attentional Mutual-Information for Long Sequence Neuron Captioning—0
Mapping Images to Sentiment Adjective Noun Pairs with Factorized Neural Nets—0
Mask-aware Text-to-Image Retrieval: Referring Expression Segmentation Meets Cross-modal Retrieval—0
Mask-free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations—0
MAT: A Multimodal Attentive Translator for Image Captioning—0
Maybe you are looking for CroQS: Cross-modal Query Suggestion for Text-to-Image Retrieval—0
Measuring directional bias amplification in image captions using predictability—0
Measuring Machine Intelligence Through Visual Question Answering—0
Measuring Representational Harms in Image Captioning—0
MedBLIP: Fine-tuning BLIP for Medical Image Captioning—0
Medical Image Captioning via Generative Pretrained Transformers—0
MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding—0
MetaToken: Detecting Hallucination in Image Descriptions by Meta Classification—0
Metropolis-Hastings Captioning Game: Knowledge Fusion of Vision Language Models via Decentralized Bayesian Inference—0
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All datasetsVizWiz 2020 test-devCOCO Captionsnocaps in-domainnocaps near-domainnocaps out-of-domainnocaps entireCOCO (Common Objects in Context)VizWiz 2020 testnocaps-XD entirenocaps-val-in-domainnocaps-val-overallnocaps-XD in-domain

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1IBM Research AICIDEr80.67—Unverified
2CASIA_IVACIDEr79.15—Unverified
3feixiangCIDEr77.31—Unverified
4wocaoCIDEr77.21—Unverified
5lamiwab172CIDEr75.93—Unverified
6RUC_AIM3CIDEr73.52—Unverified
7funasCIDEr73.51—Unverified
8SRC-B_VCLabCIDEr73.47—Unverified
9spartaCIDEr73.41—Unverified
10x-vizCIDEr73.26—Unverified
#ModelMetricClaimedVerifiedStatus
1VALORCIDER152.5—Unverified
2VASTCIDER149—Unverified
3Virtex (ResNet-101)CIDER94—Unverified
4KOSMOS-1 (1.6B) (zero-shot)CIDER84.7—Unverified
5BLIP-FuseCapCLIPScore78.5—Unverified
6mPLUGBLEU-446.5—Unverified
7OFABLEU-444.9—Unverified
8GITBLEU-444.1—Unverified
9BLIP-2 ViT-G OPT 2.7B (zero-shot)BLEU-443.7—Unverified
10BLIP-2 ViT-G OPT 6.7B (zero-shot)BLEU-443.5—Unverified
#ModelMetricClaimedVerifiedStatus
1PaLICIDEr149.1—Unverified
2GIT2, Single ModelCIDEr124.18—Unverified
3GIT, Single ModelCIDEr122.4—Unverified
4PaLICIDEr121.09—Unverified
5CoCa - Google BrainCIDEr117.9—Unverified
6Microsoft Cognitive Services teamCIDEr112.82—Unverified
7Single ModelCIDEr108.98—Unverified
8GRIT (zero-shot, no VL pretraining, no CBS)CIDEr105.9—Unverified
9FudanFVLCIDEr104.9—Unverified
10FudanWYZCIDEr104.25—Unverified
#ModelMetricClaimedVerifiedStatus
1GIT2, Single ModelCIDEr125.51—Unverified
2PaLICIDEr124.35—Unverified
3GIT, Single ModelCIDEr123.92—Unverified
4CoCa - Google BrainCIDEr120.73—Unverified
5Microsoft Cognitive Services teamCIDEr115.54—Unverified
6Single ModelCIDEr110.76—Unverified
7FudanFVLCIDEr109.33—Unverified
8FudanWYZCIDEr108.04—Unverified
9IEDA-LABCIDEr100.15—Unverified
10firetheholeCIDEr99.51—Unverified
#ModelMetricClaimedVerifiedStatus
1PaLICIDEr126.67—Unverified
2GIT2, Single ModelCIDEr122.27—Unverified
3GIT, Single ModelCIDEr122.04—Unverified
4CoCa - Google BrainCIDEr121.69—Unverified
5Microsoft Cognitive Services teamCIDEr110.14—Unverified
6Single ModelCIDEr109.49—Unverified
7FudanFVLCIDEr106.55—Unverified
8FudanWYZCIDEr103.75—Unverified
9HumanCIDEr91.62—Unverified
10firetheholeCIDEr88.54—Unverified